Increasingly, data warehouses maintain large and ever-increasing quantities of data. Extract, transform, and load (“ETL”) workflows may be used to load data into a data warehouse. An ETL workflow may include steps for extracting data from a number of sources, loading the data into a pre-production table, transforming the data into a desired format, and copying the data from the pre-production table into a production table. End-users and client applications may then access the cleaned-up data in the production table. For various reasons, an ETL workflow may be costly in terms of the time and computing resources expended to complete the workflow. One aspect of this cost involves the efficiency of copying data between database tables.